Online Course – Google’s Certified Professional Internship in Data Analytics with Python, University of Colorado Boulder

Join courses that emphasize skills in data technologies and data analytics. Gain in-depth knowledge that will prepare you for the real-world challenges of data analytics.

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קורס אונליין - התמחות מקצועית מוסמכת בניתוח נתונים עם Python של Google

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Classification
  • Regression
  • Clustering
  • Data dimensionality reduction
  • Association rules
  • Supervised learning
  • Unsupervised learning
  • Identifying exceptions

What you will learn in the course

Courses for which the course is suitable

  • Data Analyst
  • Data Scientist
  • Algorithm developer
  • Data Project Manager
  • Data Analysis Consultant
  • Data Modeling Expert
  • Information Systems Analyst
  • Business Intelligence Solution Developer
  • Data scientist
  • Research and Analysis Manager

Internship – Series of 5 courses

The Data Analytics specialization will provide a comprehensive overview of various data analysis techniques. Courses will cover a wide range of topics, including:

  • Classification
  • Regression
  • Clustering
  • Data dimensionality reduction
  • Association rules

The courses will be highly practical and will include real-life examples and case studies, which will help students develop a deeper understanding of data analysis concepts and techniques. The courses will culminate in a project that demonstrates the student’s mastery of data analysis techniques.

Applied Learning Project

The “Data Analysis Project” course allows students to apply the knowledge and skills they acquire in this specialization to carry out a practical data analysis project according to their interest. Participants will explore a variety of directions in data analysis, including:

  • Supervised learning
  • Unsupervised learning
  • Regression
  • Clustering
  • Dimension reduction
  • Association rules
  • Identifying exceptions

Throughout the modules, students will learn essential data analysis techniques and methodologies and embark on a journey from raw data to knowledge and intelligence. By completing the course, students will be proficient in data analysis, capable of applying their expertise in various projects and making data-driven decisions.

Details of the courses that make up the specialization

Classification analysis

Course 1 – 38 hours

What will you learn?

  • Understand the concept and importance of classification as one of the methods of managed learning.
  • Distinguish and describe different types of classifiers, and apply each classifier to perform binary and multi-class classification tasks on diverse datasets.
  • Evaluate the performance of classifiers, select and refine classifiers based on the characteristics of the data and learning requirements.

The skills you will acquire

  • Multiplication learning
  • Linear regression
  • Cross-validation
  • Regression
  • Scikit-Learn

Regression analysis

Course 2 – 40 hours

What will you learn?

  • Understand the principles and importance of regression analysis in supervised learning.
  • Apply cross-validation methods to evaluate model performance and improve parameters.
  • Understand the multiplication methods (bagging, boosting, stacking) and their role in increasing the accuracy of the regression model.

The skills you will acquire

  • Unsupervised learning
  • Machine learning
  • Supervised learning
  • Project planning
  • Data mining

Cluster analysis

Course 3 – 37 hours

What will you learn?

  • Understand the principles and importance of unsupervised learning, especially clustering and dimensionality reduction.
  • Apply clustering techniques to various data sets for pattern discovery and data exploration.
  • Apply principal component analysis (PCA) to reduce the dimensionality of the data and interpret the reduced space.

The skills you will acquire

  • Data clustering algorithms
  • Dimension reduction
  • K-Means Clustering
  • Principal Component Analysis (PCA)
  • Dbscan

Association rule analysis

Course 4 – 22 hours

What will you learn?

  • Understand the principles and importance of unsupervised learning methods, especially association rules and anomaly detection.
  • Relate the concepts and applications of frequency patterns and association rules in discovering interesting relationships between items.
  • Apply a variety of anomaly detection methods, including statistical and intra-distance methods to identify anomalous data points.

The skills you will acquire

  • Learning association rules
  • exceptional
  • Apriori
  • Frequency patterns
  • FP Growth

Data Analysis Project with Python

Course 5 – 18 hours

What will you learn?

  • Specify the scope and direction of a data analysis project, identify appropriate techniques and models to achieve the project’s goals.
  • Apply a variety of classification and regression algorithms and implement multiple validation techniques to improve model performance.
  • Apply algorithms for clustering, dimensionality reduction, association rule mining, and anomaly detection for unsupervised learning models.

The skills you will acquire

  • Bayesian statistics
  • Logistic regression
  • Support Vector Machine (SVM)
  • Classification
  • Decision tree

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